RESEARCH & RESOURCES

Real-Time BI: A Banking Perspective

Real-time business intelligence and analytics have been steadily gaining importance in the banking industry over the last few months. In this article, we evaluate the need for real-time BI from a banking perspective and its edge over traditional BI.

The basis of any BI/ decision support system is information readiness and availability. Initially, BI centered on individual silos of operation in an organization. The next phase focused on data centralization and creation of enterprise wide BI platforms. However, the BI solutions in an organization operated in isolation from transactional applications, with a delay between the transactional and reporting systems. With increasing focus on agility and the need to integrate business intelligence and analytical systems with transaction systems, there is an added impetus for real-time business intelligence.

The speed and quality of response of an organization to the inputs from a BI system are critical in order to quantify the investment on BI system. The ROI metrics of a BI system should focus on the actual impact of decisions taken (that is, business outcome or impact) rather than the output.

Information Value

In the financial world, “time value” is a term of extreme importance. Not just time value of money, we now add the term “time value of information.” Information also has a diminishing value. While the value of information can never touch zero, beyond a certain point, it loses value drastically.

Although this graph holds true for information in general, financial information follows this curve strictly.

Real-Time BI in Banking

Traditionally, BI systems in banking have focused on independent data warehouses used for offline reporting and analytics. BI systems have focused more on strategic objectives than on tactical and operational objectives. The adage “Information has value only to the extent it is comprehended within the time to act on it” has now come into the limelight in the banking space.

The need for strategic perspective remains significant, but there is an increasing need for a tactical/operational perspective for BI.

For instance, at an operational level, a wealth manager may be in a position to give immediate responses to clients about their portfolio performance on a given date and current investment options. The information is needed in real time and is of no worth a day later. On the other hand, the CEO of the bank can make his refinancing decisions based on his NPA portfolio as of close of the previous day. The opportunity to cross-sell in real time based on customer input is another example.

The key challenge for banks is to first assess areas where they need real-time BI. There are many areas such as fraud detection, AML, and treasury operations that require real-time BI. Many transactional system players have started providing these solutions as enhancements to the base systems. RTBI is an expensive exercise. While right skill sets and processes are essential for setting up RTBI, system & technology enhancements play the pivotal role.

It requires major enhancements in all three facets of BI:

Data gathering

Information assembly

Insight visualization

This entire cycle needs to be done in minutes.

Data Gathering

The tradition BI data warehouse refresh rates can be enhanced to provide near-real-time analytics, but this would provide insights in a few hours, not immediately. For real-time BI, small batch updates will not suffice. Rather, the system processes have to be event driven -- transactions must be updated as and when it occurs- in a matter of seconds or milliseconds. The BI database needs to be trickle fed when a transaction occurs. This is popularly known as data replication. The replication engine tracks changes in source systems using a change log and updates the BI database in a matter of seconds.

Information Assembly

Although traditional BI provides historical analysis, the basic rule in real-time BI is to compare the current-state record with the historical information and identify any anomalies. The rules engine must be powerful enough to analyze the trickle-fed data against the database history and provide information for immediate action. There are two types of rules for any operational BI system:

Rules for analysis: These rules are typically required to make patterns emerge out of existing data and set benchmark values for metrics.

Rules for exceptions: Once the benchmark values for metrics are provided, the current value of the metric is compared with benchmark/threshold levels for aberrations.

Insight Visualization

Providing insights in an intuitive and actionable manner is another commandment of any BI system. It is all the more important in a real-time BI system where immediate action items must emerge from the visualization. Real-time artifacts should not provide just anomalies in the current state. They should also provide historical trends and past occurrences of similar deviations. This would help in future planning for avoiding or handling deviations. Thus, the artifacts will provide information for operational, tactical, and strategic actions.

For instance, a portfolio manager might want to know his portfolio performance during market hours and need to know about changes and their effect on his portfolio instantaneously. In addition, knowledge of the general trend of his portfolio movement, previous deviations in portfolio value by greater than a particular percentage, and individual equity/bond returns and yields would help him reformulate his portfolio mix. One or more securities could be more volatile than others; he could set a stop loss for the certain securities.

Is Real-Time BI Really Required?

Here are some closing points to be considered while evaluating real-time BI:

Although real-time BI is good to have, real-time BI systems work with minute (or longer) lags, unlike transactional systems.

Banks must assess their requirements to understand insights that are required in real time and those that can still flow through traditional BI systems.

Transactional information that can be obtained directly from source systems must not be expected from real-time BI systems. Real-time BI systems must be restricted to providing insights to help users determine exceptions or enable comparisons with historical data.

Real-time BI will fail when data is unavailable and/or source systems are down. Before migrating to real-time BI, banks must assess completeness of data in source systems and system efficiency.